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The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally. [ABSTRACT FROM AUTHOR]

Three-phase voltage source inverters (VSIs) with output $ LC$ filter are preferred topologies to provide voltage and frequency support for islanded ac microgrids. This article proposes a finite-set quasi-sliding model predictive control (FS-QSMPC) scheme for $LC$ -filtered VSIs to improve the output-voltage quality. By explicitly including a predictive sliding-mode function into the cost function, reduced steady-state ripple and enhanced robustness are achieved compared to typical FS-MPC. Besides, theoretical analysis of stability and robustness for FS-QSMPC is given. Analytical tuning of the weighting factor is also derived to decrease the tuning effort. Comparative simulations and experiments verify the presented approach. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An improved particle swarm optimization (PSO) algorithm based on Gaussian distribution model is proposed to realize the autotuning of weighting factors for the cost function design in the model predictive control method. First, the design principle of the weighting factors in model predictive torque control for permanent magnet synchronous motor system is analyzed. Then, using the root mean square of the current error in the two-phase rotating coordinate system and the system switching frequency as references, the objective function of the particles in the PSO is designed by considering the main control goals of reducing the torque ripple, the current total harmonic distortion, and the switching frequency. The Gaussian individual optimal distribution model is used to update the particle position on the structure of the conventional PSO algorithm. The experimental results show that the proposed method can solve the problem of weighting factors design as it reduces the switching frequency of the system while achieving excellent steady-state performance. [ABSTRACT FROM AUTHOR]

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter. [ABSTRACT FROM AUTHOR]

Recently, fault-tolerance control (FTC) methods have been proposed to further improve the reliability of T-type three-level converters. However, the application of model predictive control (MPC) in FTCs is rare, and the neutral point (NP) voltage imbalance before an open-switch fault occurs has not been solved. This article focuses on these two problems and proposes two FTC control methods: sequential MPC and sequential model predictive tolerance control. Both methods are implemented by designing a sequential predictive control that first considers the NP voltage balance and then grid current tracking. Through the staggered operation of the two methods, a T-type grid-connected converter with an LCL filter can operate normally after an open-switch fault occurs. Moreover, both methods can achieve the NP voltage balance and excellent grid current outputs, eliminate the time-consuming selection of weighting factors, and reduce the number of calculation loops compared with the traditional model predictive tolerance control, thereby improving the control flexibility. The performance and comparison of the open-switch FTC with respect to the NP voltage oscillations, total harmonic distortion of the grid current, and pole voltage were demonstrated using numerous simulation and experimental results. [ABSTRACT FROM AUTHOR]

Direct model predictive control (DMPC) has become an effective control alternative for the grid-tied modular multilevel converter. However, classical DMPC of the grid-tied MMC relies on accurate system model. System parameter variations will deteriorate the control performances of power, grid current, and circulating current. Besides, the inherent fewer switching actions of DMPC will also lead to poor circulating current performance. To solve these issues, this work proposes a robust DMPC solution for the grid-tied MMC. Novelties lie in two aspects. First, a virtual-flux (VF) based online inductance estimator is proposed, which utilizes the VF estimation error caused by inductance deviation. It achieves fast and accurate estimation of AC equivalent inductance, and reduces the dependency on system parameters. Thereafter, we propose a hybrid predictive control frame with a simple proportional-resonant (PR) controller, which considerably suppresses the circulating current. Finally, experimental results verify the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

Three-level T-type converters (3LT2C) with inductance–capacitance–inductance filters have high power quality of grid current, particularly in the low-voltage system. However, the switching loss and conduction losses degrade the overall efficiency of 3LT2C. The generated heat due to the losses endangers the reliability of the device and shortens its service life. To relieve the power losses as well as maintain high-quality current output, a low-loss-tolerant sequential model predictive control (LL-TSMPC) is proposed. First, based on the energy loss analysis under switching transitions per commutation, the prediction models of both switching loss and conduction loss are established. Second, the total loss reduction is considered as one control objective in LL-TSMPC. Finally, because multiobjective model predictive control (MPC) requires selecting appropriate weighting factors, complicating the design to solve this problem, TSMPC-based lexicographic optimization is introduced to eliminate the tradeoff factors and simplify the MPC controller design. The proposed method is tested on the hardware platform with the rated power of a 10-kW prototype. The experimental results show that the LL-TSMPC method can effectively reduce the switching losses while maintaining the high power quality of the grid current. [ABSTRACT FROM AUTHOR]

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions. [ABSTRACT FROM AUTHOR]

This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC. [ABSTRACT FROM AUTHOR]

Aging and temperature changes in the passive components of an LCL-filter grid connected converter system (GCCs) may lead to parameter uncertainties, which can in turn influence its modeling accuracy for finite-control-set model predictive control (FCS-MPC). The presence of model errors will change the resonance point and deteriorate the power quality of the grid current, in turn degrading the active damping performance. In this situation, there is a serious possibility that the GCCs may malfunction and automatically disconnect from the grid, causing great challenges to the system stability. To solve this problem, first, prediction error analysis in FCS-MPC due to the model parameter errors is presented. Second, to achieve high accuracy and fast filter parameter estimation in utility, an adaptive online parameter identification method based on gradient descent optimization (GDO) has been proposed. Finally, to further reduce the searching time needed by the optimal iteration step, a variable iteration step searching method based on the root-mean-square-prop (RMSprop) GDO method is proposed. Experimental studies of an LCL-GCCs prototype in the laboratory have been conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An improved two-stage model predictive current control (MPCC) for three-phase three-level converters in the paper, which can simultaneously achieve common-mode voltage (CMV) reduction, effective dc-link capacitor voltage control, and decreased computational burden, is proposed. Compared to the other current control strategies, it has the capability of handling multiple control objectives and possesses a simple structure. Furthermore, it greatly reduces the CMV since the peak CMVs of all the candidate voltage vectors are constrained to be less than one sixth of the dc-link voltage. In addition, an optimal two-stage voltage vector selection mechanism is employed in the proposed method. It helps to significantly reduce the computational complexity. Simulation and experimental results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The extended state observer (ESO) has been found as an efficient solution for the model sensitivity in the model-predictive control (MPC). Generally, the ESO is used for disturbance rejection when the voltage reference is the output of the control law. The observed disturbance is subtracted from the control law. Thus, using disturbance rejection for finite-set MPC is a challenge when it is utilized by a cost function consisting of the errors of the torque and the flux. In this research, a modified disturbance rejection method is used in a feedforward shape to improve the robustness of the finite-set model-predictive torque control. In this regard, the motor parameters have not appeared in the torque prediction algorithm. Only a rough approximation of the motor parameters is needed to design the ESO, which means that the proposed method has a very low dependence on the motor parameters. Besides, a thorough tuning guideline is proposed to tune the local parameters based on the convergence analysis of the ESO by using the self-stable region approach and the Lyapunov function. The performance of the proposed MPC scheme is evaluated through simulations and experimental tests. The results of the proposed method are compared with the classic MPC results. [ABSTRACT FROM AUTHOR]

This article presents an optimal model predictive flux control (MPFC) for a two-level inverter fed induction motor. Integrating discrete SVM into FCS-MPFC enhances the performance of the IM drive. However, conventional DSVM-MPFC requires to enumerate and evaluate a higher number of virtual vectors in the prediction loop. In this paper, a high-efficient and low complexity voltage selection method is proposed to reduce the number of candidate voltage vectors from 38 to 15 without any suboptimality. Both steady-state and transient performances of the proposed method remain the same as the 38-vector based conventional DSVM-MPFC, producing the same cost-function values in all operating conditions. Furthermore, an online switching frequency reduction technique is proposed to achieve a minimum commutation per inverter vector change within each sampling cycle and between adjacent cycles. By appropriately arranging the sequence of real voltage vectors in each sampling cycle, a lower average switching frequency is achieved. The proposed switching frequency reduction method decreases the switching losses without compromising the performance of DSVM-MPFC as only the applied sequences of the real voltage vectors are optimized. Experimental studies are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The efficiency of the linear induction machine (LIM) is considerably low due to the large air-gap length and the additional losses resulting from the end-effects. However, so far, a little concentration has been done to increase the efficiency of this kind of motor. Therefore, the main objectives of this article are to eliminate the weighting factor, reduce the calculation steps, and increase the efficiency of the LIM aiding with a proposed finite-set model predictive direct flux control (FS-MPDFC), depending upon the maximum thrust per ampere (MTPA) strategy. The proposed FS-MPDFC is only based on the primary flux-linkage with the help of a novel cost function that does not require any weighting factors in order to considerably reduce the calculation steps. Moreover, the proposed FS-MPDFC is better than the model predictive current control in reducing the calculation step and decreasing the dependence on the transformation matrix and the machine parameters. In order to illustrate the effectiveness of the proposed MTPA-based FS-MPDFC, a comparison between the FS-MPDFC, with and without MTPA, and the previously suggested control techniques is presented and discussed. Moreover, a test bench platform of two 3-kW arc induction machines is constructed to emulate the drive performance of the actual LIM. Comprehensive simulation and experiments have fully demonstrated that the proposed FS-MPDFC with MTPA has lower calculation step, lower thrust ripple by 7%, and lower primary flux-linkage ripple by 0.4% compared to those of the FS-MPDTC. Moreover, the efficiency-based FS-MPDFC with MTPA is increased by 4% compared to that of the FS-MPDFC without MTPA. [ABSTRACT FROM AUTHOR]

Voltage source inverters with output LC filter enable a sinusoidal output voltage with low harmonics, suitable for islanded ac microgrid or uninterruptible power supply applications. Conventional finite-set model predictive voltage control (MPVC) applies only a single switching vector per control period, leading to a variable switching frequency and significant output ripple. This article resolves these issues by proposing an improved MPVC with optimal switching sequence (OSS-MPVC). First, an improved vector switching sequence is defined, aiming to reduce the output-voltage ripple with a constant switching frequency. Then, to tackle the difficulty in extending the OSS to high-order systems due to the coupling effect of the output filter, a generalized “one-step estimation” solution is proposed, which directly associates the control-variable gradients with the vector switching sequence. To further enhance the output-voltage tracking accuracy, intersample dynamics are taken into account in the cost function. The control delay and dead-time compensation are also considered. Simulations and experimental results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the conventional continuous control set model predictive current control (CCS-MPCC), such as large overshoot and poor robustness, an extended surface-mounted permanent magnet synchronous motor (SPMSM) model-based multistep error tracking CCS-MPCC (MSET-CCSMPCC) is proposed in this article. First, a traditional CCS-MPCC is derived based on the conventional SPMSM model and its robustness is analyzed by considering the parameter mismatches. Second, an extended SPMSM model is given by incorporating the lumped disturbances into one disturbance part. Third, a sliding mode differentiator improved fast terminal sliding mode disturbance observer is designed to track the disturbances. Fourth, by compensating the extended SPMSM model for the estimated d- and q-axes disturbances, an extended SPMSM model-based CCS-MPCC (EXM-CCSMPCC) is designed. However, the EXM-CCSMPCC has serious step response overshoot. Fifth, an extended SPMSM model-based single step error tracking CCS-MPCC is presented, whose dynamic response and steady-state performances deteriorate when the overshoot is reduced. Finally, an MSET-CCSMPCC is proposed to reduce the overshoot and improve the robustness while maintaining excellent dynamic and steady-state performances. Experiments are implemented on a field-programmable gate array based hardware system to verify the excellent performances of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a simple model predictive current control strategy is proposed for four-leg inverters to deliver power to the (un)balanced/(non)linear loads. Unlike classical control schemes, the proposed method does not require current PI controllers or a complex modulation stage. The discrete-time model of the inverter, filter, and load are used to predict the future behavior of the load currents for each of the 16 possible switching states. The control method chooses a switching state that minimizes the error between the output currents and their references. The performance of the proposed method has been investigated for perturbations in the filter and load parameters. The neutral-leg switching frequency reduction algorithm is also proposed to improve the efficiency of the converter. The feasibility of the proposed method is verified in this first part, through simulation results. [ABSTRACT FROM PUBLISHER]